Self-driving vehicle simulation using computer vision and machine learning Available to Purchase

In the modern era where computers are taking over the world, the vehicles are focused to be fully computerized to give human driver a safe and relaxed driving. As humans naturally make mistakes, autonomous vehicles are mainly focused on errorless driving to make the journey safe. The goal is to use computer vision as well as machine learning algorithms to let the vehicles drive themselves. After capturing each frame from the simulation platform, computer vision recognizes the lanes and barriers. The driving vehicle will proceed based on decisions made after autonomously identifying barriers and traffic signals. Euro Truck Simulator is used to test the model against the simulation platform. Several optimizers, including Adam, Adagrad, Adamax, Adadelta, and SGD, as well as activation functions, including sigmoid, exponential linear unit (ELU), and rectified linear unit (RELU), are used to train the model. The observations show that the greater accuracy of 0.9889 is achieved with the ELU activation function and the Adam optimizer.